A not-for-income Business, IEEE is the earth's premier technical Specialist organization committed to advancing engineering for the good thing about humanity.
The following article content are merged in Scholar. Their mixed citations are counted only for the first write-up.
This "Cited by" depend includes citations to the next articles in Scholar. The ones marked * might be different with the post during the profile.
下面的文章将在学术搜索中合并。它们的合并引用次数只会计入第一篇文章。
The global financial system is experiencing one of the most difficult era of local climate alter outside of what is evident while in the pre-industrial age. Whilst Africa's share of global greenhouse gas (GHG) is nominal, the…
The next articles are merged in Scholar. Their mixed citations are counted just for the 1st article.
Toward assault-resistant route mutation for VANETs: an on the web and adaptive multiagent reinforcement Studying tactic
A not-for-profit Firm, IEEE is the earth's most significant complex Qualified Group devoted to advancing technologies for the good thing about humanity.
A QoE-pushed multicast system with segment routing—A novel multimedia targeted traffic engineering paradigm
The existing period is facing a Problem concerning partaking the essentialities of Strength methods to Jiahao Shen KCL achieve economic prosperity on account of the following environmental difficulties. For that reason, sectors…
An optimized quantum minimum amount searching algorithm with certain-good results likelihood and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do all-natural source dependence, renewable Electricity, and technological improvements amplify or reduce the pollution noises?
Scanning radar forward-seeking superresolution imaging according to the Weibull distribution for a sea-area goal
This "Cited by" count includes citations to the subsequent articles in Scholar. Those marked * can be unique through the report while in the profile.
此“引用引文的出版物数量”计数包含了对学术搜索中以下文章的引用次数。带有*标记的引文可能与个人学术档案中的文章有所区别。
How you can disturb network reconnaissance: a shifting target protection approach based on deep reinforcement learning